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Semantic Parsing
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Semantic Parsing is the task of transducing natural language utterances into formal meaning representations. The target meaning representations can be defined according to a wide variety of formalisms. This include linguistically-motivated semantic representations that are designed to capture the meaning of any sentence such as λ-calculus or the abstract meaning representations. Alternatively, for more task-driven approaches to Semantic Parsing, it is common for meaning representations to represent executable programs such as SQL queries, robotic commands, smart phone instructions, and even general-purpose programming languages like Python and Java.
Source: Tranx: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/megagonlabs/cypherbench
공식 코드CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era
https://github.com/mercatorhq/dubo-sql
공식 코드Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQL
https://github.com/microsoft/ContextualSP
공식 코드How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context
https://github.com/microsoft/EMNLP2019-Split-And-Recombine
공식 코드https://github.com/microsoft/Table-Pretraining
공식 코드TAPEX: Table Pre-training via Learning a Neural SQL Executor
https://github.com/microsoft/few_shot_adaptation_for_parsing_contextual_utterances_with_llms
공식 코드Few-Shot Adaptation for Parsing Contextual Utterances with LLMs
https://github.com/microsoft/semantic_parsing_with_constrained_lm
공식 코드https://github.com/microsoft/synqo
공식 코드MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQL
https://github.com/mnschmit/unsupervised-graph-text-conversion
공식 코드An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing
https://github.com/mohammadrezapourreza/few-shot-nl2sql-with-prompting
공식 코드DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
https://github.com/molingbo/inspired
공식 코드Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction
https://github.com/mswellhao/dangle
공식 코드Disentangled Sequence to Sequence Learning for Compositional Generalization
https://github.com/muyeby/amrbart
공식 코드https://github.com/najoungkim/COGS
공식 코드COGS: A Compositional Generalization Challenge Based on Semantic Interpretation
https://github.com/namednil/f-then-r
공식 코드Compositional Generalisation with Structured Reordering and Fertility Layers
https://github.com/namednil/multiset-perm
공식 코드Compositional Generalization without Trees using Multiset Tagging and Latent Permutations
https://github.com/namednil/step
공식 코드Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations
https://github.com/naver/sqlova
공식 코드A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization
https://github.com/neuripspublishingresearchcode/select-sql
공식 코드SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQL
https://github.com/niansong1996/lever
공식 코드LEVER: Learning to Verify Language-to-Code Generation with Execution
https://github.com/niansong1996/wassp
공식 코드https://github.com/nickswalker/gpsr-command-understanding
공식 코드https://github.com/nih-card/biomedsql
공식 코드BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases
https://github.com/niklaswretblad/the-effects-of-noise-in-text-to-SQL
공식 코드Understanding the Effects of Noise in Text-to-SQL: An Examination of the BIRD-Bench Benchmark
https://github.com/nitishgupta/allennlp-semparse
공식 코드https://github.com/nju-websoft/SPARQA
공식 코드SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Bases
https://github.com/nl2sql-empirical/nl2sql_error_empirical
공식 코드https://github.com/nzjin/odtqa
공식 코드Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval
https://github.com/omriabnd/cds_syntax_semantics
공식 코드Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech
https://github.com/osu-nlp-group/auto-sql-correction
공식 코드https://github.com/osu-nlp-group/fl4semanticparsing
공식 코드Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms
https://github.com/osu-nlp-group/llm-planning-eval
공식 코드When is Tree Search Useful for LLM Planning? It Depends on the Discriminator
https://github.com/osu-nlp-group/text2sql-error-detection
공식 코드https://github.com/pagrawal-ml/Unified-Semantic-Parsing
공식 코드https://github.com/parajain/dynamic_context
공식 코드Conversational Semantic Parsing using Dynamic Context Graphs
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